Clinical Remission as Defined by the Mayo Score
Bibliographic record
Abstract
Reply: In his comment on our article, Professor Peyrin-Biroulet raises an important point and astutely identifies some of the deficiencies of the Mayo score and inconsistencies in its use.1 We agree with his assertion that combining clinical and endoscopic parameters into a single index is inherently flawed. We also agree that we should be moving away from this dogmatic method of evaluating disease activity in ulcerative colitis as this type of approach increases the variability of evaluations and decreases statistical efficiency.2 It is also not fully validated and infrequently applied in clinical practice. However, we disagree regarding the possible solution to this problem suggested by Professor Peyrin-Biroulet. Instead of simply dividing the clinical and endoscopic Mayo components to derive additional unvalidated definitions for remission, we would be in favor of using patient-reported outcomes and endoscopy as co-endpoints. In our opinion, this approach offers several advantages.3 Primarily, by the nature of their generation, patient-reported outcomes can be considered more clinically meaningful than a physicians' subjective symptom assessment as they are created and defined by patients themselves. This means that they are more readily applicable to clinical practice and, in combination with endoscopy as an objective measure of inflammation, offer the opportunity to remedy the existing disconnect in outcome assessment between trials and practice. The benefit of such convergence in goal-setting and progress evaluation is likely to grow as “treat-to-target” algorithms becoming more widely adopted.4
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".